System for cardiac impairment detection based on signal regularity

Abstract

A system for heart performance characterization and abnormality detection comprises an input processor and a signal processor. The input processor receives first sampled data representing a first signal portion of a heart activity related signal and second sampled data representing a second signal portion of a heart activity related signal. The signal processor determines distribution data associated with degree of similarity between the first and second signal portions by determining a difference between (a) values derived by applying a first function to mean adjusted sampled values of the first signal portion and (b) values derived by applying a second function to mean adjusted sampled values of the second signal portion. In response to the determined distribution data, the signal processor initiates generation of a message associated with the degree of similarity between the first and second signal portions.

Description

[0001]This is a non-provisional application of provisional application Ser. No. 61 / 489,704 filed May 25, 2011, by H. Zhang et al.FIELD OF THE INVENTION[0002]This invention concerns a system for heart performance characterization and abnormality detection involving determining distribution data associated with degree of similarity between first and second signal portions of a heart activity related signal.BACKGROUND OF THE INVENTION[0003]Myocardial ischemia and infarction analysis and detection within ventricular tissue, for example, is used in the management of cardiac disorders and irregularities, which are caused by a lack of blood and oxygen, in heart tissue. Usually, surface ECG signal analysis based on waveform morphology and time domain parameters is utilized for myocardial ischemia and infarction detection and characterization, such as by determination of ST segment or T wave changes (repolarization). However known systems fail to provide a comprehensive quantitative method f...

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Problems solved by technology

However known systems fail to provide a comprehensive quantitative method for myocardial status detection and characterization, such as for severity characterization of an ongoing myocardial ischemia event with chest pain and discomfort.

Additionally, known systems for cardiac ischemia and infarction identification and analysis based on ECG signals are typically subjective and need extensive expertise for accurate pathology interpretation and proper cardiac rhythm management.

Coronary Artery Disease (CAD) and heart-related problems and cardiac arrhythmias are severe frequently fatal conditions.

However, waveform morphology and time domain parameter analysis are sometimes subjective and time-consuming, and require extensive expertise and clinical experience for accurate interpretation and proper cardiac rhythm management.

Cardiac electrophysiological activities and signals (ECG and ICEG signals) are time varying and known signal analysis typically fails to localize a precise malfunction and identify its severity and an associated trend of cardiac events (e.g. of myocardial ischemia and infarction), such as cardiac pathology irregularity stages and arrhythmia occurrence.

Known systems for myocardial ischemia and infarction analysis typically need a benign signal as a baseline for threshold determination of events and lack reliability, stability and accuracy, especially in emergency cases.

Known methods for MI analysis focus on an event which is more qualitative involving detection and evaluation of MI occurrence and lack a capability for quantitative characterization of MI severity.

Furthermore, known ischemia event detection systems may cause a false alarm due to reliance on single parameter analysis such as measurement of magnitude of an ST segment.

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